Generalized fractional processes in terms of Gegenbauer polynomials and GARCH (Generalized Autoregressive Conditional Heteroscedastic) errors is introduced and derived as a time series model. A related simulation study of the proposed model depicts statistical properties of the new class established in terms of the realization, sample autocorrelation function, the- oretical autocorrelation function, partial autocorrelation function and the spectral density function.
How to Cite:
Dissanayake, G. & Peiris, S., (2012). Generalized Fractional Processes with Conditional Heteroscedasticity. Sri Lankan Journal of Applied Statistics. 12, pp.1–12. DOI: http://doi.org/10.4038/sljastats.v12i0.4964